Neo is a Python package for working with electrophysiology data in Python, together with support for reading a wide range of neurophysiology file formats, including Spike2, NeuroExplorer, AlphaOmega, Axon, Blackrock, Plexon, Tdt, and support for writing to a subset of these formats plus non-proprietary formats including HDF5.
The goal of Neo is to improve interoperability between Python tools for analyzing, visualizing and generating electrophysiology data by providing a common, shared object model. In order to be as lightweight a dependency as possible, Neo is deliberately limited to represention of data, with no functions for data analysis or visualization.
Neo is used by a number of other software tools, including SpykeViewer (data analysis and visualization), Elephant (data analysis), the G-node suite (databasing), PyNN (simulations), tridesclous (spike sorting) and ephyviewer (data visualization). OpenElectrophy (data analysis and visualization) uses an older version of neo.
Neo implements a hierarchical data model well adapted to intracellular and extracellular electrophysiology and EEG data with support for multi-electrodes (for example tetrodes). Neo's data objects build on the quantities package, which in turn builds on NumPy by adding support for physical dimensions. Thus Neo objects behave just like normal NumPy arrays, but with additional metadata, checks for dimensional consistency and automatic unit conversion.
A project with similar aims but for neuroimaging file formats is NiBabel.
- Home page: http://neuralensemble.org/neo
- Mailing list: https://groups.google.com/forum/?fromgroups#!forum/neuralensemble
- Documentation: http://neo.readthedocs.io/
- Bug reports: https://github.com/NeuralEnsemble/python-neo/issues
For installation instructions, see doc/source/install.rst
To cite Neo in publications, see CITATION.txt
| copyright: | Copyright 2010-2020 by the Neo team, see doc/source/authors.rst. |
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| license: | 3-Clause Revised BSD License, see LICENSE.txt for details. |
Development of Neo has been partially funded by the European Union Sixth Framework Program (FP6) under grant agreement FETPI-015879 (FACETS), by the European Union Seventh Framework Program (FP7/2007-2013) under grant agreements no. 269921 (BrainScaleS) and no. 604102 (HBP), and by the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreements No. 720270 (Human Brain Project SGA1), No. 785907 (Human Brain Project SGA2) and No. 945539 (Human Brain Project SGA3).